Impact ofFeature Selection Techniques forEEG-Based Seizure Classification

Title

Impact ofFeature Selection Techniques forEEG-Based Seizure Classification

Creator

Najmusseher; Umme Salma M.

Description

A neurological condition called epilepsy can result in a variety of seizures. Seizures differ from person to person. It is frequently diagnosed with fMRI, magnetic resonance imaging and electroencephalography (EEG). Visually evaluating the EEG activity requires a lot of time and effort, which is the usual way of analysis. As a result, an automated diagnosis approach based on machine learning was created. To effectively categorize epileptic seizure episodes using binary classification from brain-based EEG recordings, this study develops feature selection techniques using a machine learning (ML)-based random forest classification model. Ten (10) feature selection algorithms were utilized in this proposed work. The suggested method reduces the number of features by selecting only the relevant features needed to classify seizures. So to evaluate the effectiveness of the proposed model, random forest classifier is utilized. The Bonn Epilepsy dataset derived from UCI repository of Bonn University, Germany, the CHB-MIT dataset collected from the Childrens Hospital Boston and a real-time EEG dataset collected from EEG clinic Bangalore is accustomed to the proposed approach in order to determine the best feature selection method. In this case, the relief feature selection approach outperforms others, achieving the most remarkable accuracy of 90% for UCI data and 100% for both the CHB-MIT and real-time EEG datasets with a fast computing rate. According to the results, the reduction in the number of feature characteristics significantly impacts the classifiers performance metrics, which helps to effectively categorize epileptic seizures from the brain-based EEG signals into binary classification. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes in Networks and Systems, Vol-613 LNNS, pp. 197-207.

Date

2023-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Classification; EEG; Epilepsy; Feature selection; Machine learning; Seizure

Coverage

Najmusseher, Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, 560029, India; Umme Salma M., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, 560029, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981199378-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-19-9379-4_16

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161139900&doi=10.1007%2f978-981-19-9379-4_16&partnerID=40&md5=064e19f44da6cd6cf588bc7b840475c3